线性回归模型
in 机器学习 with 2 comments

线性回归模型

in 机器学习 with 2 comments
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

rng = np.random
# 训练参数
learning_rate = 0.01
training_epochs = 1000
display_step = 50
# 训练数据
train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]# 值的数量

# 构造输入
X = tf.placeholder("float")
Y = tf.placeholder("float")

# 设置权重
W = tf.Variable(rng.randn(),name = "weight")# rng.randn 返回一个浮点数
b = tf.Variable(rng.randn(),name = "bias")

# 构造一个线性的模型
pred = tf.add(tf.multiply(X,W),b)

# 构建误差函数
cost = tf.reduce_sum(tf.pow(pred - Y,2))/(2*n_samples)#均方误差。 Y为实际的位置,pred为预测的位置
# 设置训练目标
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Note:使用梯度下降法来逐渐减少cost的值

# 初始化
init = tf.global_variables_initializer()

# 开始训练
with tf.Session() as sess:
    sess.run(init)

    # 训练所有的数据
    for epoch in range(training_epochs):# 迭代次数
        for(x,y) in zip(train_X,train_Y):# 把x和y组合成一个坐标
            sess.run(optimizer,feed_dict={X:x,Y:y})

            if(epoch+1) % display_step == 0:
                c = sess.run(cost,feed_dict={X:train_X,Y:train_Y})
                print("Epoch:",'%04d'%(epoch+1),"cost","{:.9f}".format(c),"W=",sess.run(W),"b=",sess.run(b))
    print("完成训练目标!")

    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
    print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

    # Graphic display
    plt.plot(train_X, train_Y, 'ro', label='Original data')# 参数分别为x,y的坐标
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()


    test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

    print("Testing... (Mean square loss Comparison)")
    testing_cost = sess.run(
        tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),
        feed_dict={X: test_X, Y: test_Y})  # same function as cost above
    print("Testing cost=", testing_cost)
    print("Absolute mean square loss difference:", abs(
        training_cost - testing_cost))

    plt.plot(test_X, test_Y, 'bo', label='Testing data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()
Responses
  1. ICHENY

    小伙子加油

    Reply
    1. @ICHENY

      互粉啊小哥哥

      Reply